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2019-07-23 22:55:58 +02:00
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doc/pub/Intro2Course/html/._Intro2Course-bs000.html
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<!-- ------------------- main content ---------------------- -->
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<center><h1>Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
<center>
<b>Morten Hjorth-Jensen</b> [1, 2]
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<p>
<!-- institution(s) -->
<center>[1] <b>Department of Physics, University of Oslo</b></center>
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Jul 23, 2019</h4></center> <!-- date -->
<br>
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<li><a href="._Intro2Course-bs003.html">4</a></li>
<li><a href="._Intro2Course-bs004.html">5</a></li>
<li><a href="._Intro2Course-bs005.html">6</a></li>
<li><a href="._Intro2Course-bs006.html">7</a></li>
<li><a href="._Intro2Course-bs007.html">8</a></li>
<li><a href="._Intro2Course-bs008.html">9</a></li>
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<footer>
<a href="http://..."><img width="250" align=right src="http://..."></a>
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-->
<center style="font-size:80%">
<!-- copyright --> &copy; 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
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doc/pub/Intro2Course/html/._Intro2Course-bs001.html
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<p>
<div class="panel panel-default">
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<li> Thursday: First lecture: Presentation of the course, aims and content</li>
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
<li> Friday: Linear regression</li>
<li> Computer lab: Tuesday. First time: Tuesday August 27.</li>
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doc/pub/Intro2Course/html/._Intro2Course-bs002.html
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<li> Lectures: Thursday (12.15pm-2pm) and Friday (12.15pm-2pm).</li>
<li> Weekly reading assignments needed to solve projects and exercises.</li>
<li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
<li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
<li> Computerlab: Tuesday (8am-6pm), VB IT-auditorium 3</li>
<li> Weekly plans and all other information are on the official webpage.</li>
<li> No final exam, three projects that are graded and have to be approved.</li>
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<li><a href="._Intro2Course-bs004.html">5</a></li>
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<li><a href="._Intro2Course-bs006.html">7</a></li>
<li><a href="._Intro2Course-bs007.html">8</a></li>
<li><a href="._Intro2Course-bs008.html">9</a></li>
<li><a href="._Intro2Course-bs009.html">10</a></li>
<li><a href="._Intro2Course-bs010.html">11</a></li>
<li><a href="._Intro2Course-bs003.html">&raquo;</a></li>
</ul>
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<!-- include javascript, jQuery *first* -->
<script src="https://ajax.googleapis.com/ajax/libs/jquery/1.10.2/jquery.min.js"></script>
<script src="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/js/bootstrap.min.js"></script>
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<footer>
<a href="http://..."><img width="250" align=right src="http://..."></a>
</footer>
-->
<center style="font-size:80%">
<!-- copyright only on the titlepage -->
</center>
</body>
</html>
doc/pub/Intro2Course/html/._Intro2Course-bs003.html
+177
View File
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<span class="icon-bar"></span>
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<a class="navbar-brand" href="Intro2Course-bs.html">Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</a>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
<!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<ul>
<li> Three compulsory projects. Electronic reports only using <a href="https://devilry.ifi.uio.no/" target="_self">devilry</a> to hand in projects and <a href="https://github.com/" target="_self">Git</a> for repository and all your material.</li>
<li> Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
<ol type="a"></li>
<li> For the last project Each group/participant submits a proposal or works with suggested (by us) proposals for the project.</li>
<li> If possible, we would like to organize the last project as a workshop where each group makes a poster and presents this to all other participants of the course</li>
<li> Poster session where all participants can study and discuss the other proposals.</li>
<li> Based on feedback etc, each group finalizes the report and submits for grading.</li>
</ol>
<li> Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programmin languages. All source codes discussed during the lectures can be found at the webpage and <a href="https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" target="_self">github address</a> of the course.</li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
</ul>
</li>
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<p>
<div class="panel panel-default">
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<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<p>
<b>Teachers :</b>
<ol>
<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_self">Hanna Svennevik</a></li>
<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_self">Morten Hjorth-Jensen</a></li>
<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_self">Lucas Charpentier</a></li>
<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_self">Stian Bilek</a></li>
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<tr><td align="center"><b> day </b></td> <td align="center"><b> Time </b></td> </tr>
</thead>
<tbody>
<tr><td align="center"> Group 1: Tuesday </td> <td align="center"> 8am-10am </td> </tr>
<tr><td align="center"> Group 2: Tuesday </td> <td align="center"> 10am-12pm </td> </tr>
<tr><td align="center"> Group 3: Tuesday </td> <td align="center"> 12pm-2pm </td> </tr>
<tr><td align="center"> Group 4: Tuesday </td> <td align="center"> 2pm-4pm </td> </tr>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<p>
<div class="panel panel-default">
<div class="panel-body">
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ol>
<li> Project 1: September 30 (graded with feedback)</li>
<li> Project 2: November 4 (graded with feedback)</li>
<li> Project 3: December 2 (graded with feedback)</li>
</ol>
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
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<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
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<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ul>
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
<li> Be capable of extending the acquired knowledge to other systems and cases</li>
<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
<li> Learn about various neural networks and deep learning methods for supervised and unsupervised learning</li>
<li> Learn about about decision trees and random forests</li>
<li> Learn about support vector machines and kernel transformations</li>
<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
</ul>
</div>
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<p>
<p>
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<a class="navbar-brand" href="Intro2Course-bs.html">Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</a>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
<!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
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<h2 id="___sec9" class="anchor">Other courses on Data science and Machine Learning at UiO </h2>
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The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_self"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
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<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_self">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_self">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_self">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_self">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_self">STK-IN4300 Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_self">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li>
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_self">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_self">IN5400/INF5860 Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_self">TEK5040 Deep learning for autonomous systems</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_self">STK4051 Computational Statistics</a></li>
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html" target="_self">STK4021 Applied Bayesian Analysis and Numerical Methods</a></li>
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